Wavelet based approach for Fusarium corn kernels recognition using spectral data processing

Abstract A new approach for Fusarium diseased corn kernels recognition based on wavelet transformation and near infrared spectral analysis is proposed in the paper. The near infrared diffuse reflectance was used for assessment the corn kernels changes. Continuous wavelet transformation with mexican hat function estimated the spectral data and formed the classification features. Two classifiers were used - linear and probabilistic neural network for classifying the test set of corn kernels. The results show that this approach could be used for Fusarium corn kernels recognition.